#!/usr/bin/env python

# --------------------------------------------------------
# R-FCN
# Copyright (c) 2016 Yuwen Xiong
# Licensed under The MIT License [see LICENSE for details]
# Written by Yuwen Xiong
# --------------------------------------------------------

"""
Demo script showing detections in sample images.

See README.md for installation instructions before running.
"""

import _init_paths
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
from fast_rcnn.nms_wrapper import nms
from utils.timer import Timer
# import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse

CLASSES = ('__background__',
           'echinus', 'seastar', 'shell', 'trepang')

NETS = {'ResNet-101': ('ResNet-101',
                  'resnet101_rfcn_final.caffemodel'),
        'ResNet-50': ('ResNet-50',
                  # 'resnet50_rfcn_final.caffemodel')}
                  'resnet50_rfcn_ohem_iter_4000.caffemodel')}


def vis_detections(im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    # print(dets[:, -1])
    if len(inds) == 0:
        # print(dets[:, -1])
        return
    # im = im[:, :, (2, 1, 0)]
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]
        cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255-class_name*50, class_name*50, class_name*50), 2)
        cv2.putText(im, str(score), (bbox[0], bbox[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255-class_name*50, class_name*50, class_name*50), 2)
    cv2.imshow("test", im)
    cv2.waitKey(1)

def demo(net, frame):
    """Detect object classes in an image using pre-computed object proposals."""

    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(net, frame)
    timer.toc()
    # print ('Detection took {:.3f}s for '
    #        '{:d} object proposals').format(timer.total_time, boxes.shape[0])

    # Visualize detections for each class
    CONF_THRESH = 0.8
    NMS_THRESH = 0.3
    for cls_ind, cls in enumerate(CLASSES[1:]):
        cls_ind += 1 # because we skipped background
        cls_boxes = boxes[:, 4:8]
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes,
                          cls_scores[:, np.newaxis])).astype(np.float32)
        keep = nms(dets, NMS_THRESH)
        dets = dets[keep, :]
        vis_detections(frame, cls_ind, dets, thresh=CONF_THRESH)

def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Faster R-CNN demo')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
                        default=0, type=int)
    parser.add_argument('--cpu', dest='cpu_mode',
                        help='Use CPU mode (overrides --gpu)',
                        action='store_true')
    parser.add_argument('--net', dest='demo_net', help='Network to use [ResNet-101]',
                        choices=NETS.keys(), default='ResNet-101')

    args = parser.parse_args()

    return args

if __name__ == '__main__':
    cfg.TEST.HAS_RPN = True  # Use RPN for proposals

    args = parse_args()

    prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
                            'rfcn_end2end', 'test_agnostic.prototxt')
    caffemodel = os.path.join(cfg.DATA_DIR, 'rfcn_models',
                              NETS[args.demo_net][1])

    if not os.path.isfile(caffemodel):
        raise IOError(('{:s} not found.\n').format(caffemodel))

    if args.cpu_mode:
        caffe.set_mode_cpu()
    else:
        caffe.set_mode_gpu()
        caffe.set_device(args.gpu_id)
        cfg.GPU_ID = args.gpu_id
    net = caffe.Net(prototxt, caffemodel, caffe.TEST)

    print '\n\nLoaded network {:s}'.format(caffemodel)

    # Warmup on a dummy image
    im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
    for i in xrange(2):
        _, _= im_detect(net, im)

    cap = cv2.VideoCapture('./data/demo/echinus_RUAS.avi')
    while(1):
        ret, frame = cap.read()
        # cv2.imshow("test", frame)
        # cv2.waitKey(1)
        demo(net, frame)
